A Study on the Effectiveness of Deep Learning-Based Anomaly Detection Methods for Breast Ultrasonography

Author:

Yun Changhee1,Eom Bomi1ORCID,Park Sungjun2,Kim Chanho2,Kim Dohwan3ORCID,Jabeen Farah2,Kim Won Hwa4,Kim Hye Jung4,Kim Jaeil2ORCID

Affiliation:

1. National Information Society Agency, Daegu 41068, Republic of Korea

2. School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Republic of Korea

3. Department of Artificial Intelligence, Kyungpook National University, Daegu 41566, Republic of Korea

4. Department of Radiology, Kyungpook National University Chilgok Hospital, Kyungpook National University, Daegu 41404, Republic of Korea

Abstract

In the medical field, it is delicate to anticipate good performance in using deep learning due to the lack of large-scale training data and class imbalance. In particular, ultrasound, which is a key breast cancer diagnosis method, is delicate to diagnose accurately as the quality and interpretation of images can vary depending on the operator’s experience and proficiency. Therefore, computer-aided diagnosis technology can facilitate diagnosis by visualizing abnormal information such as tumors and masses in ultrasound images. In this study, we implemented deep learning-based anomaly detection methods for breast ultrasound images and validated their effectiveness in detecting abnormal regions. Herein, we specifically compared the sliced-Wasserstein autoencoder with two representative unsupervised learning models autoencoder and variational autoencoder. The anomalous region detection performance is estimated with the normal region labels. Our experimental results showed that the sliced-Wasserstein autoencoder model outperformed the anomaly detection performance of others. However, anomaly detection using the reconstruction-based approach may not be effective because of the occurrence of numerous false-positive values. In the following studies, reducing these false positives becomes an important challenge.

Funder

Kyungpook National University Research Fund

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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